研究人员开发了 DiRe-RAPIDS，一种新的降维技术，与 UMAP 和 t-SNE 等现有方法相比，它能更好地保留高维数据的全局拓扑结构。DiRe-RAPIDS 在一个旨在评估噪声流形拓扑忠实度的新基准上进行了调优。在一个大型的 arXiv 论文嵌入数据集上，DiRe-RAPIDS 在可比的速度下，比 UMAP 保持了显著更多的拓扑结构。此外，还引入了一个新的框架，用于定量和可视化分析参数化投影方法的局部邻域不稳定性，并展示了其在基于 UMAP 和 t-SNE 的神经网络投影器上的有效性。 AI
影响 引入了新的高维数据可视化方法，可能改进 AI 研究中大型数据集的分析。
排序理由 该集群包含两篇 arXiv 论文，介绍了降维的新方法和评估框架。
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Semantic communication systems for goal-oriented transmission must protect task-relevant information not only through source compression but also via physical layer mapping. Existing approaches decouple constellation design and semantic encoding, exposing critical symbols to chan…
arXiv:2605.05209v1 Announce Type: new Abstract: Neural networks that land in flat regions of the loss landscape tend to generalise better than those in sharp regions. Sharpness-Aware Minimisation exploits this to improve generalisation. But function-preserving reparameterisation …
arXiv:2604.25209v1 Announce Type: new Abstract: Dimensionality reduction methods such as UMAP and t-SNE are central tools for visualising high-dimensional data, but their local-neighborhood objectives can preserve sampling noise while distorting global topology. We show that stan…
Dimensionality reduction methods such as UMAP and t-SNE are central tools for visualising high-dimensional data, but their local-neighborhood objectives can preserve sampling noise while distorting global topology. We show that standard local metrics reward this noise memorisatio…
Parametric projections let analysts embed new points in real time, but input variations from measurement noise or data drift can produce unpredictable shifts in the 2D layout. Whether and where a projection is locally stable remains largely unexamined. In this paper, we present a…